In addition to parameter and state sharing, a separate network with a slot gating mechanism was introduced by goo2018slot to model the interaction between SF and IC more explicitly (Figure REF  Middle). In the encoder, a slot context vector for each time step, \(c^{S}_{i}\) , and a global intent context vector \(c^{I}\)  are computed using an attention mechanism [1]}. The slot-gate \(g^{s}\)  is computed as a function of \(c^{S}_{i}\)  and \(c^{I}\) , \(g^{s} = \sum v \cdot tanh(c^{S}_{i} + W\cdot c^{I} )\) . Then, \(g^{s}\)  is used as a weight between \(h_i\)  and \(c^{S}_i\)  to compute \(y^{slot}_{i}\)  as follows: \(y^{slot}_i=\texttt {softmax}(W(h_i + g^{s}\cdot c^{S}_i))\) . Larger \(g^{s}\)  indicates a stronger correlation between \(c^{S}_{i}\)  and \(c^{I}\) .
